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A Multipopulation-Based Multiobjective Evolutionary Algorithm.

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    This study introduces a novel multipopulation genetic algorithm with a unique migration process. Simulations confirm it enhances optimization performance and validates a new Markov model for multiobjective problems.

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    Area of Science:

    • * Evolutionary Computation
    • * Optimization Theory
    • * Computational Intelligence

    Background:

    • * Multipopulation strategies are effective components in evolutionary algorithms for complex optimization tasks.
    • * Existing multiobjective genetic algorithms (MOGAs) can benefit from enhanced information sharing between subpopulations.
    • * Biological processes offer inspiration for novel information exchange mechanisms in artificial intelligence.

    Purpose of the Study:

    • * To propose a new multipopulation-based multiobjective genetic algorithm (MOGA) incorporating a unique cross-subpopulation migration process.
    • * To develop the first Markov model for a multipopulation MOGA, providing an exact mathematical framework for multiobjective populations.
    • * To evaluate the proposed method's effectiveness in improving optimization performance and its applicability to other multiobjective evolutionary algorithms (MOEAs).

    Main Methods:

    • * Development of a novel multipopulation MOGA featuring a biologically inspired cross-subpopulation migration strategy.
    • * Derivation of a Markov model to mathematically describe the dynamics of the multipopulation MOGA.
    • * Simulation experiments on multiobjective test problems and comparison with existing MOEAs using IEEE benchmarks.

    Main Results:

    • * Simulation results validated the derived Markov model for the multipopulation MOGA.
    • * The proposed multipopulation approach demonstrated improved optimization capabilities compared to single-population MOGAs.
    • * Extending single-population MOEAs to multipopulation versions using the proposed method yielded enhanced optimization performance.

    Conclusions:

    • * The novel cross-subpopulation migration strategy effectively enhances information sharing and optimization performance in MOGAs.
    • * The derived Markov model offers a rigorous mathematical foundation for analyzing multipopulation multiobjective evolutionary algorithms.
    • * The multipopulation paradigm is a viable and effective extension for improving the performance of various MOEAs.